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dyffusion
[NeurIPS 2023] A Dynamics-informed Diffusion Model for Spatiotemporal ForecastingtorchTS
Time series forecasting with PyTorchLIMO
generative model for drug discoveryECCO
Turbulent-Flow-Net
Turbulent flow network source codeHDR-IL
Equivariant-Net
LieGAN
Teleportation-Optimization
[ICLR 2024] Improving Convergence and Generalization Using Parameter SymmetriesDynamic-Adaptation-Network
AutoSTPP
Automatic Integration for Neural Spatio-Temporal Point Process models (AI-STPP) is a new paradigm for exact, ef๏ฌcient, non-parametric inference of point process. It is capable of learning complicated underlying intensity functions, like a damped sine wave.DeepSTPP
Spatiotemporal_UQ
Uncertainty Quantification for Deep Spatiotemporal ForecastingCopulaCPTS
Code for Copula conformal prediction paper (ICLR 2024)Approximately-Equivariant-Nets
Multi-Fidelity-Deep-Active-Learning
AutoODE-DSL
V2V-traffic-forecast
L4DC2021 code repositorymrtl
Multiresolution Tensor LearningLab-Wiki
Knowledge SharingSymmetry-Teleportation
[NeurIPS 2022] Symmetry Teleportation for Accelerated OptimizationHierarchical-Neural-Processes
Zihao-s-Toolbox
A toolbox of shared utilities across different projects.nautilus_tutorial
Interactive-Neural-Process
FS-CAP
few-shot compound activity regressionDIVE
Disentangled Imputed Video autoEncoder (DIVE)AutoNPP
Efficient computation of temporal point process intensity using automatic integrationGradient-Flow-Symmetry
[ICLR 2023] Symmetries, flat minima, and the conserved quantities of gradient flowir2rgb
IR to RGB video translationMFRNP
[ICML 2024] Multi Fidelity Residual Neural ProcessMRTL-ST
cs-6140-fall-2018
Final project page for CS 6140GPU-Benchmark
group-net
rose-stl-lab.github.io
UCSD RoseLab Server DocumentationLove Open Source and this site? Check out how you can help us